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Conference Paper: Spatio-temporal topic models for check-in data

TitleSpatio-temporal topic models for check-in data
Authors
KeywordsCheck-in
Spatio-temporal
Topic Model
Issue Date2015
PublisherIEEE Computer Society.
Citation
The 15th IEEE International Conference on Data Mining (ICDM 2015), Atlantic City, NJ., 14-17 November 2015. In Conference Proceedings, 2015, p. 889-894 How to Cite?
AbstractTwitter, together with other online social networks, such as Facebook, and Gowalla have begun to collect hundreds of millions of check-ins. Check-in data captures the spatial and temporal information of user movements and interests. To model and analyze the spatio-temporal aspect of check-in data and discover temporal topics and regions, we propose two spatio-temporal topic models: Downstream Spatio-Temporal Topic Model (DSTTM) and Upstream Spatio-Temporal Topic Model (USTTM). Both models can discover temporal topics and regions. We use continuous time to model check-in data, rather than discretized time, avoiding the loss of information through discretization. In order to capture the property that user's interests and activity space will change over time, we propose the USTTM, where users have different region and topic distributions at different times. We conduct experiments on Twitter and Gowalla data sets. In our quantitative analysis, we evaluate the effectiveness of our models by the perplexity, the accuracy of POI recommendations, and user prediction, demonstrating that our models achieve better performance than the state-of-the-art models. © 2015 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/234881
ISBN
ISSN

 

DC FieldValueLanguage
dc.contributor.authorLiu, Y-
dc.contributor.authorEster, M-
dc.contributor.authorHu, B-
dc.contributor.authorCheung, DWL-
dc.date.accessioned2016-10-14T13:49:51Z-
dc.date.available2016-10-14T13:49:51Z-
dc.date.issued2015-
dc.identifier.citationThe 15th IEEE International Conference on Data Mining (ICDM 2015), Atlantic City, NJ., 14-17 November 2015. In Conference Proceedings, 2015, p. 889-894-
dc.identifier.isbn978-146739503-8-
dc.identifier.issn1550-4786-
dc.identifier.urihttp://hdl.handle.net/10722/234881-
dc.description.abstractTwitter, together with other online social networks, such as Facebook, and Gowalla have begun to collect hundreds of millions of check-ins. Check-in data captures the spatial and temporal information of user movements and interests. To model and analyze the spatio-temporal aspect of check-in data and discover temporal topics and regions, we propose two spatio-temporal topic models: Downstream Spatio-Temporal Topic Model (DSTTM) and Upstream Spatio-Temporal Topic Model (USTTM). Both models can discover temporal topics and regions. We use continuous time to model check-in data, rather than discretized time, avoiding the loss of information through discretization. In order to capture the property that user's interests and activity space will change over time, we propose the USTTM, where users have different region and topic distributions at different times. We conduct experiments on Twitter and Gowalla data sets. In our quantitative analysis, we evaluate the effectiveness of our models by the perplexity, the accuracy of POI recommendations, and user prediction, demonstrating that our models achieve better performance than the state-of-the-art models. © 2015 IEEE.-
dc.languageeng-
dc.publisherIEEE Computer Society.-
dc.relation.ispartofIEEE International Conference on Data Mining Proceedings-
dc.rightsIEEE International Conference on Data Mining Proceedings. Copyright © IEEE Computer Society.-
dc.rights©2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectCheck-in-
dc.subjectSpatio-temporal-
dc.subjectTopic Model-
dc.titleSpatio-temporal topic models for check-in data-
dc.typeConference_Paper-
dc.identifier.emailCheung, DWL: dcheung@cs.hku.hk-
dc.identifier.authorityCheung, DWL=rp00101-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ICDM.2015.45-
dc.identifier.scopuseid_2-s2.0-84963542118-
dc.identifier.hkuros268749-
dc.identifier.spage889-
dc.identifier.epage894-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 161027-

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